Multi-Variate Pattern Analysis for
Identification of Metabolites that are Predictive of Malignant Transformation
in Gliomas using HRMAS Spectra from Image Guided Tissue Samples

1Electrical Engineering
& Computer Science, University of California, Berkeley, CA, United
States; 2Department of Radiology & Biomedical Imaging,
University of California, San Francisco, CA, United States; 3Department
of Neurological Surgery, University of California, San Franisco, CA, United
States

In this study, we applied multivariate pattern
recognition methods to HRMAS spectra from image guided tissue samples in order
to identify metabolites that are predictive of malignant transformations in
gliomas and to accurately detect those patients exhibiting malignant
transformations. Our method extracts a small subset of features in the HRMAS
spectra and uses it to build a parsimonious model capable of discriminating
between patients with different tumor grades with over 90% accuracy. The
features used in our model are traced back to known metabolites in the
corresponding chemical shift range, thus identifying a useful set of
metabolites to acquire in-vivo.